Hologram stability evaluation for Microsoft HoloLens
Bibliographic record
Abstract
Augmented reality (AR) has an increasing presence in the world of image-guided interventions which is amplified by the availability of consumer-grade head-mounted display (HMD) technology. The Microsoft® HoloLensTM optical passthrough device is at the forefront of consumer technology, as it is the first un-tethered head mounted computer (HMC). It shows promise of effectiveness in guiding clinical interventions, however its accuracy and stability must still be evaluated for the clinical environment. We have developed an evaluative protocol for the HoloLensTM using an optical measurement device to digitize the perceived pose of the rendered hologram. This evaluates the ability of the HoloLensTM to maintain the hologram in its intended pose. The stability is measured when actions are performed that may cause a shift in the holograms’ pose due to errors in its simultaneous localization and mapping. An emphasis is placed on actions that are more likely to be performed in a clinical setting. This will be used to determine the most applicable use cases for this technology in the future and how to minimize errors when in use. Our results show promise of this device’s potential for intraoperative clinical use. Further analysis must be performed to evaluate other potential sources of hologram disruption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".